Journal of Applied Psychology
2009, Vol. 94, No. 3, 742–755
© 2009 American Psychological Association
0021-9010/09/$12.00 DOI: 10.1037/a0015497
Does It Pay to Be Smart, Attractive, or Confident (or All Three)?
Relationships Among General Mental Ability, Physical Attractiveness,
Core Self-Evaluations, and Income
Timothy A. Judge, Charlice Hurst, and Lauren S. Simon
University of Florida
The authors investigated core self-evaluations and educational attainment as mediating mechanisms for
the influence of appearance (physical attractiveness) and intelligence (general mental ability) on income
and financial strain. The direct effects of core self-evaluations on financial strain, as well as the indirect
effects through income, were also considered. Longitudinal data were obtained as part of a national study,
the Harvard Study of Health and Life Quality, and proposed models were evaluated with structural
equation modeling. Results supported a partially mediated model, such that general mental ability and
physical attractiveness exhibited both direct and indirect effects on income, as mediated by educational
attainment and core self-evaluations. Finally, income negatively predicted financial strain, whereas core
self-evaluations had both a direct and an indirect (through income) negative effect on financial strain.
Overall, the results suggest that looks (physical attractiveness), brains (intelligence), and personality
(core self-evaluations) are all important to income and financial strain.
Keywords: core self-evaluations, attractiveness, income, intelligence, general mental ability
Harper, 2000). No prior research has considered the possible role
of individual differences in the appearance–income relationship;
however, attractiveness is related to a number of personal characteristics that, it is often argued, are developed through a process of
expectancy confirmation (Langlois, 1986; Langlois et al., 2000). In
this process, stereotypes regarding attractiveness elicit expectations that lead to consistently differential judgment and treatment.
These outcomes are then internalized and cause development of
differential behavior, traits, and self-views (see also Darley &
Fazio, 1980).
In this study, we argue that educational attainment and core
self-evaluations— or general self-concept—are influenced by attractiveness and should mediate the effects of attractiveness on
income. One of the strengths of our approach is the use of longitudinal data. Although many arguments for a causal effect of
appearance on individual differences have been made, few have
provided evidence for such claims. Furthermore, although previous studies examining the influence of attractiveness on income
have controlled for the effects of education, few have considered
the influence of cognitive ability. A model including both attractiveness and intelligence should, therefore, explain more variance
in income and shed light on the relative importance of each.
A final purpose of this study is to consider how self-concept
affects financial strain, which influences productivity (Joo &
Grable, 2004; Kim, Sorhaindo, & Carman, 2006) and is implicated
in overall well-being (Diener & Biswas-Diener, 2002; van Praag,
Frijters, & Ferrer-i-Carbonell, 2003). Income alone does not explain why some are vexed with financial worries and others are
unbothered. Because of this, researchers have also considered
financial knowledge, attitudes, goals, and behaviors (Joo &
Grable, 2004; Vera-Toscano, Ateca-Amestoy, & Serrano-DelRosal, 2006; Webley & Nyhus, 2001). Self-concept has been
Few personal characteristics are more associated with success in
life than brains and beauty. Although the raw materials of neither
seem particularly manipulable (both are heritable and relatively
stable across the lifespan), the benefits of being beautiful—and the
penalties for homeliness—seem particularly unfair. Countless parents have assured their children that it is “the inside that counts,”
with the “inside” presumably including one’s intelligence and
personality. Indeed, these characteristics do influence career success (Ng, Eby, Sorensen, & Feldman, 2005). Yet, although the
inside clearly counts, a plethora of empirical research has demonstrated that when it comes to income, attractiveness makes a
difference too (Biddle & Hamermesh, 1998; Hamermesh &
Biddle, 1994; Harper, 2000; Langlois et al., 2000; Mobius &
Rosenblat, 2006).
Although mechanisms for the effect of intelligence on income
seem nearly self-evident, there is considerably more ground to
cover with regard to the influence of looks. Little is known about
why there are income discrepancies among attractive, averagelooking, and unattractive people. The primary mediators that have
been examined to date are employer and customer discrimination
(Biddle & Hamermesh, 1998; Hamermesh & Biddle, 1994;
Harper, 2000; Hosoda, Stone-Romero, & Coats, 2003; Mobius &
Rosenblat, 2006), occupational sorting (Biddle & Hamermesh,
1998; Hamermesh & Biddle, 1994; Harper, 2000), and differential
outcomes in the marriage market (Hamermesh & Biddle, 1994;
Timothy A. Judge, Charlice Hurst, and Lauren S. Simon, Department of
Management, University of Florida.
Correspondence concerning this article should be addressed to Timothy
A. Judge, Warrington College of Business Administration, University of
Florida, PO Box 117165, Gainesville, FL 32611-7165. E-mail:
[email protected]
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SMART, ATTRACTIVE, OR CONFIDENT
largely absent from the literature. We suggest that unlike attractiveness and intelligence, core self-evaluations influence financial
strain not only indirectly via their influence on income but also
directly. Here we argue that self-concept is a key antecedent of
financial well-being because of its effects on subjective appraisals
of one’s financial situation.
Figure 1 displays the hypothesized model. It links attractiveness
and general mental ability to income through educational attainment and core self-evaluations. Finally, income and core selfevaluations are expected to directly affect financial strain. In the
following sections, we explain the theoretical basis for the model
and provide support for the hypothesized linkages embedded
within, focusing on the central role of individual differences in
financial well-being.
Theory and Hypotheses
Direct Effects of Attractiveness on Income, Education,
and Core Self-Evaluations
That attractiveness positively affects income has been well
established in prior research. Langlois et al.’s (2000) meta-analysis
revealed that 68% of attractive adults were above the mean on
occupational success—which included income—versus 32% of
unattractive adults. Subsequent research has established further
support for the relationship between attractiveness and earnings
(Harper, 2000). Thus, consistent with past findings,
Hypothesis 1a: Physical attractiveness is positively associated
with income.
It seems unlikely that one’s educational prospects would also be
influenced by attractiveness. Yet, a positive link between attractiveness and educational attainment has been demonstrated in one
large-sample study, albeit cross-sectional (Umberson & Hughes,
1987). From an early age, attractive people receive more encouragement in academic endeavors than unattractive people. Their
intellectual and academic competence are judged more positively
General mental
ability
(Test composite)
(Jackson, Hunter, & Hodge, 1995; Langlois et al., 2000), even by
people who know them well (Langlois et al., 2000). They receive
more attention and are subject to more positive interactions with
others (e.g., instructional assistance from teachers, positive reactions from adults and children) and fewer negative ones (e.g.,
punishment by teachers, aggression, negative feedback; Langlois
et al., 2000). For example, in one study, school psychologists were
less likely to refer an attractive, poorly achieving third grader to
remedial classes than a less attractive student (Elovitz & Salvia,
1982).
Being treated better in academic settings may translate into
substantial advantages for attractive people. Social support increases academic achievement and motivation (Perry & Weinstein, 1998; Robbins et al., 2004; Wentzel, 1998), both of which
are positively associated with educational attainment (Erikson,
Goldthorpe, Jackson, Yaish, & Cox, 2005; Mullen, Goyette, &
Soares, 2003; Robbins et al., 2004). Indeed, attractive children
actually do earn higher grades and standardized test scores;
Langlois et al. (2000) found a corrected correlation of ␳ ⫽ .20
for the relationship between attractiveness and measures of
academic performance among children. This performance differential might ultimately result in higher levels of education
among attractive adults, consistent with Umberson and Hughes’s
(1987) findings. Grades and test scores from elementary through
high school positively predict the likelihood of obtaining a high
school diploma and completing postsecondary education (Erikson
et al., 2005; Guay, Larose, & Boivin, 2004; Powell & Steelman,
1993; Reynolds, Ou, & Topitzes, 2004; Reynolds, Temple, Robertson, & Mann, 2001). For example, Guay et al. (2004) found that
teacher-rated math and verbal achievement of students in Grades
3–5 was positively associated with the level of education they had
reached 10 years later. In another study (Reynolds et al., 2004),
literacy scores in kindergarten were positively associated with high
school completion by age 20, whereas low reading and math
achievement through age 15 negatively affected high school completion.
Educational
attainment
(Time 1)
Income
(Time 2)
Demographics
(age, race, sex)
Physical
attractiveness
(ratings)
743
Core selfevaluations
(Time 1)
Figure 1.
Hypothesized model.
Financial strains
(Time 3)
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JUDGE, HURST, AND SIMON
In sum, attractive children are the beneficiaries of more resources supportive of academic achievement, which makes them
more likely to finish high school and go to college.
Hypothesis 1b: Physical attractiveness is positively associated
with educational attainment.
Core self-evaluations are individuals’ basic evaluations of themselves and their success in and control of their life (Judge, Locke,
& Durham, 1997). It is a latent construct indicated by four lower
order traits: self-esteem, generalized self-efficacy, emotional stability (neuroticism), and locus of control. Though frequently studied alone, these traits share a considerable amount of variance that
can be characterized as a general evaluation of one’s sense of
competence, deservingness, and control over success in life
(Judge, Erez, Bono, & Thoresen, 2002, 2003).
No study to date has examined the possible role of self-concept
as a mediator of the effects of attractiveness. Though core selfevaluations are a trait, traits have situational properties that make
them subject to prediction and variation over time (Roberts, Walton, & Viechtbauer, 2006). Attractiveness should influence global
self-concept in three ways. First, global self-worth is strongly
influenced by the warmth and approval of others (Harter, 2006);
such feedback is (often unwittingly) given to attractive people as
early as the first few days of life (Langlois, Ritter, Casey, & Sawin,
1995). Global self-concept is also predicated on perceived competence in areas of personal importance (Harter, 1993; W. James,
1892), which likely include appearance, given its salience and its
pervasive emphasis in multiple domains in our society. Finally,
Andreoletti, Zebrowitz, and Lachman (2001) have argued that the
stigma of being unattractive, often accompanied by negative judgment and treatment, may lead to the perception of a high level of
external constraint, one aspect of general locus of control.
Harter (1993) characterized the correlations between appearance
and global self-worth throughout life as “staggeringly high” (p.
95), ranging between .70 and .80. Langlois et al. (2000) found a
much more modest relationship, but attractiveness was still positively related to observed self-confidence/self-esteem in children
(␳ ⫽ .12) and self-reported self-confidence/self-esteem (␳ ⫽ .12),
competence (␳ ⫽ .13), and mental health (␳ ⫽ .16) in adults. In an
experimental study, Mobius and Rosenblat (2006) found that attractiveness was positively related to confidence in one’s abilities.
On the basis of longitudinal samples of adolescents and adults,
Zebrowitz, Collins, and Dutta (1998) found that men judged as
attractive in their 30s were more emotionally stable than men
judged as average or unattractive, although they did not find the
same relationships for adult women or for adolescents of either
sex. Finally, Andreoletti et al. (2001) found some support for a
positive influence of attractiveness on control beliefs.
Hypothesis 1c: Physical attractiveness is positively associated
with core self-evaluations.
Direct Effects of General Mental Ability on Income,
Education, and Core Self-Evaluations
As with attractiveness, ample research has demonstrated a positive influence of cognitive ability on income (Heckman, Stixrud,
& Urzua, 2006; Herrnstein & Murray, 1994; Ng et al., 2005;
Scullin, Peters, Williams, & Ceci, 2000). Ng et al.’s (2005) metaanalysis, for instance, revealed a corrected correlation of ␳ ⫽ .27
between salary and cognitive ability. Likewise, Scullin, Peters,
Williams, and Ceci (2000) found that cognitive ability, measured
in young adulthood, predicted total income and wages 16 years
later.
There is also considerable evidence that intelligence affects
educational attainment (Ceci, 1991; Herrnstein & Murray, 1994;
Neisser et al., 1996). Intelligence positively influences learning
and success at skill acquisition (Carver, 1990; Gottfredson, 1997).
This leads to a positive spiral in which, as Ceci and Williams
(1997) suggested, relatively more intelligent students receive psychosocial and instrumental support as a result of their successes.
As noted earlier, such positive treatment provides the basis for
further achievement and continued schooling.
Hypothesis 2a: General mental ability is positively associated
with income.
Hypothesis 2b: General mental ability is positively associated
with educational attainment.
As with all traits, the main source of core self-evaluations is
genetic, as revealed in the substantial heritability of core indicators
such as self-esteem (Neiss, Sedikides, & Stevenson, 2006); however, as with attractiveness, we believe that core self-evaluations
will be influenced by intelligence, or general mental ability. Gottfredson (1997) argued that intelligence creates “functional competence,” or the achievement of commonly valued social ends. These
ends are not limited to academic pursuits but vary across a broad
array of work and life outcomes (see Gordon, 1997) such as job
performance and occupational success (Judge, Higgins, Thoresen,
& Barrick, 1999; Schmidt & Hunter, 1998), social class (Deary,
Batty, & Gale, 2008), health (Gottfredson & Deary, 2004), and
financial well-being and employment (Herrnstein & Murray,
1994). The success that intelligent individuals find in many areas
of life should carry over to their self-concept.
Hypothesis 2c: General mental ability is positively associated
with core self-evaluations.
Direct Effects of Education and Core Self-Evaluations
on Income
Previous scholarship has established that educational attainment
exerts a positive influence on income (Heckman et al., 2006;
Herrnstein & Murray, 1994; Ng et al., 2005; Perna, 2003; Scullin
et al., 2000). Education should improve income by providing
stronger credentials to signal one’s value in the market and by
strengthening the skills and knowledge needed to obtain the rewards associated with effective performance.
Hypothesis 3: Educational attainment is positively associated
with income.
A longitudinal examination by Judge and Hurst (2007) found
that core self-evaluations measured in young adulthood were positively associated with income 23 years later. Although this is the
only study of the link between direct measures of the broad core
self-evaluations construct and income, research on the income
SMART, ATTRACTIVE, OR CONFIDENT
effects of the specific core traits also suggests that core selfevaluations should positively affect income. Ng et al. (2005) found
that both self-esteem and internal locus of control had positive,
nonzero associations with income. In a sample of over 5,000
individuals, Waddell (2006) found that low self-esteem measured
in adolescence was related negatively to wages earned 14 years
later.
Hypothesis 4: Core self-evaluations are positively associated
with income.
Mediating Roles of Educational Attainment and Core
Self-Evaluations
Educational attainment and core self-evaluations are among the
factors that likely link attractiveness and general mental ability
with income. We expect partial mediation by education and core
self-evaluations in the cases of both attractiveness and intelligence.
As mentioned earlier, previous researchers have demonstrated that
at least some of the income differences between attractive and
unattractive people are due to employer and customer discrimination, marital success, and occupational sorting (Biddle &
Hamermesh, 1998; Hamermesh & Biddle, 1994; Harper, 2000;
Hosoda et al., 2003; Mobius & Rosenblat, 2006). Yet, attractive
people might also earn more partly because, as argued above, they
become more educated and view themselves more positively.
Hypothesis 5a: Educational attainment partially mediates the
relationship between physical attractiveness and income.
Hypothesis 5b: Core self-evaluations partially mediate the
relationship between physical attractiveness and income.
Like attractiveness, intelligence influences a number of other
factors that play a role in earnings such as job performance
(Schmidt & Hunter, 1998), employment stability (Leventhal, Graber, & Brooks-Gunn, 2001), and job knowledge acquisition
(Hunter, 1986; Ree, Carretta, & Teachout, 1995). Nevertheless,
recent research (Heckman et al., 2006; Scullin et al., 2000) has
provided evidence that educational attainment mediates the relationship between intelligence and income. Furthermore, although
the relationship has not been explored previously, we believe that
the higher core self-evaluations of intelligent people also contribute to their income advantage.
Hypothesis 6a: Educational attainment partially mediates the
relationship between general mental ability and income.
Hypothesis 6b: Core self-evaluations partially mediate the
relationship between general mental ability and income.
Income, Core Self-Evaluations, and Financial Strain
It is clear why income would negatively predict financial strain.
Ample income leaves one less vulnerable to economic shocks and
more able to cover the mundane costs of living. Yet, the experience of one’s financial status as stressful may be as much a matter
of perception as it is a reaction to an objectively adverse state of
affairs. As evidence, Vera-Toscano et al. (2006) found that subjective appraisals based on future expectations and peer compari-
745
sons improved prediction of financial satisfaction beyond income
alone.
From the perspective of the transactional theory of stress (Lazarus & Folkman, 1984), appraisals of circumstances as threatening
or challenging are influenced by individual differences like selfesteem, self-efficacy, and sense of control. On the basis of this
notion, Judge et al. (1997) argued that core self-evaluations influence attitudes by coloring interpretations of external circumstances
and events. Indeed, a study of full-time health care workers with
the same employer revealed that core self-evaluations were negatively associated with perceptions of organizational constraints,
leading to lower levels of burnout (Best, Stapleton, & Downey,
2005). Other research has found that general self-efficacy is associated with appraisal of situations as challenges rather than threats
(Luszczynska, Gutiérrez-Doña, & Schwarzer, 2005). Moreover,
individuals high in neuroticism experience more negative events,
but more importantly, they react more strongly to such events and
choose less effective coping mechanisms (Bolger & Zuckerman,
1995). Thus, core self-evaluations should affect the psychological
aspects of financial strain because those with negative core selfevaluations are more susceptible to strains in general.
The link between core self-evaluations and financial strains may
be substantiated on behavioral grounds as well. Crocker and
Luhtanen (2003) found that appearance-based self-esteem predicted financial problems among college students, presumably
because those with negative self-esteem tended to spend more on
clothing and products to enhance their appearance. Thus, individuals with positive core self-evaluations should be less likely to
experience financial strains because they earn more, appraise their
financial situation more sanguinely, and engage in financial behaviors that minimize the types of problems that can lead to stress.
Hypothesis 7a: Income is negatively associated with financial
strain.
Hypothesis 7b: Core self-evaluations are negatively associated with financial strain.
Hypothesis 7c: Income partially mediates the relationship
between core self-evaluations and financial strain.
Method
Participants and Procedure
The data were obtained as part of a national study, the Harvard
Study of Health and Life Quality, which was initiated in 1995. The
national study— called the Midlife Development in the United
States study (Brim et al., 2003)—was based on a national probability sample of noninstitutionalized, English-speaking adults, ages
25–75 years (M ⫽ 47.8, SD ⫽ 13.1). During the national study, the
Boston area was oversampled to create a subset to be used for the
present study, the Boston In-Depth Study of Management Processes in Midlife (Lachman, 2004). The Boston study began six
months after the national study and consisted of three interviews
over three periods spaced 6 months apart, as well as tests (cognitive ability, cortisol) and photograph taking. A total of 429 potential participants were contacted.
Contact persons were informed that the survey was being carried out through the Harvard Medical School and that it was
746
JUDGE, HURST, AND SIMON
designed to study health and well-being during the middle years of
life. After the purpose of the study was explained, the contact
person was asked to generate a listing of all household members
ages 25–74 years, from which a random participant was selected.
Once the phone call to the contact person was completed, participants were mailed a survey, which included a boxed pen and a
check for $20. A reminder postcard was mailed to all participants
3 days after the initial questionnaire. A second questionnaire with
a cover letter urging participants to return the questionnaire was
mailed 2 weeks later to all participants who had not returned the
questionnaire by that time. Reminder telephone calls were made 2
weeks later to all participants who had still not returned the
questionnaire.
Of the 429 individuals who were selected to participate, 38
subsequently were unreachable, and a total of 302 participants
responded at Time 1, for a 77.2% response rate. Just under two
thirds (62.6%) of these participants were married, 58.9% were
men, and the majority (93.3%) were Caucasian. Less than a third
(28.6%) had either less than or equivalent to a high school diploma, 24.9% had some college education, and almost half
(46.5%) had a bachelor’s degree or more. Of the 302 individuals
who participated at Time 1, 288 (95.3%) participated at Time 2,
and 289 (95.7%) participated at Time 3. The sample size was
further reduced by missing data such as unavailability of test
scores or photographs. All things considered, after listwise deletion, complete data were available for 191 participants. Demographic results for the final sample remained similar to those of the
full sample, with 62.8% of the final sample listed as married,
64.3% listed as men, and 95.9% listed as Caucasian. Further, as in
the original sample, 28.6% had either less than or equal to a high
school diploma, 23.4% had some college education, and 48% had
at least a bachelor’s degree.
Measures
Financial strains. Financial strains were measured with a
four-item scale, asked during the Time 3 interview. The four items
were as follows: (a) “Please tell me whether you have experienced
financial problems (e.g., low income, or heavy debts) since we last
spoke (Time 2)”; (b) “How often have you had money/financial
problems”: 1 (never), 2 (several times a year), 3 (once a month),
4 (several times a month), 5 (once a week), 6 (several times a
week), or 7 (almost every day)? (c) “How often has money/
finances gone well”: 1 (never), 2 (several times a year), 3 (once a
month), 4 (several times a month), 5 (once a week), 6 (several
times a week), or 7 (almost every day)? (d) “How much stress do
you get from money/finances”: 1 (none), 2 (a little), 3 (some), or
4 (a lot)? Because the items were measured with different response
scales, they were standardized prior to averaging, and the third
item was reverse scored. The coefficient alpha reliability estimate
of this scale was .86.
Income. At Time 2, participants’ income was measured with
the interview question “Please tell me which letter indicates your
total yearly household income from all sources, before taxes.”
Participants were then shown a card that divided income into 36
groups (each designated with a letter), ranging from $0 (A, scored
1) to $35,000 –$39,999 (BB, scored 18), to $1,000,000 or more
(LL, scored 36).
Core self-evaluations. Core self-evaluations were measured
with a 15-item scale. Because the items were evaluated on two
response scales (some items on a 1–5 scale, others on a 1–7 scale),
the items were standardized prior to computing the total score.
Sample items included “I often feel helpless in dealing with the
problems of life” (reverse scored), “When I look at the story of my
life, I am pleased with how things have turned out so far,” and “In
many ways I feel disappointed about my achievements in life”
(reverse scored). The coefficient alpha reliability estimate of this
scale was .85.
Because this study was initiated before Judge et al.’s (2003) core
self-evaluations scale (CSES) was developed, we sought to validate the measure we used by determining whether it converges
with the CSES. To do so, we concurrently administered the present
15-item scale, along with the 12-item CSES and three Big Five
measures, to 795 undergraduates enrolled in an introductory management course at a large southeastern university. The correlation
between the 15-item scale (␣ ⫽ .91 in this sample) and the 12-item
CSES (␣ ⫽ .88 in this sample) was r ⫽ .72 (rc ⫽ .80). Moreover,
when compared with various measures of the Big Five traits, both
core self-evaluations scales (the CSES and the 15-item measure
used in this study) exhibited similar patterns of relationships.
Specifically, in correlating both measures with an average of three
Big Five measures—the minimarkers (Saucier, 1994), the Big Five
Inventory (John & Srivastava, 1999), and the International Personality Item Pool (Goldberg, 1996)—we devised the correlations
shown in Table 1. Thus, it appears that the core self-evaluations
measure used in this study was reliable and displayed a high level
of convergent validity with the CSES.
Educational attainment. At Time 1, educational attainment
was measured on a 12-point scale: 1 (some grade school), 2 (junior
high/eighth grade), 3 (some high school), 4 (general equivalency
diploma), 5 (graduated from high school), 6 (1 to 2 years of
college, no degree), 7 (3 years of college, no degree), 8 (2-year
college degree), 9 (4-year college degree), 10 (some graduate
school), 11 (master’s degree), and 12 (doctoral-level degree).
Physical attractiveness. Attractiveness was measured with ratings of photographs. In the Midlife Development in the United
States study, front and profile photographs were taken of each
participant in a carefully controlled manner (i.e., photos were taken
from the same distance with the same background, individuals’
clothing was obscured with a cloak, participants were instructed to
pose with a neutral facial expression, front and profile photographs
were taken). All photographs were rated on a scale from 1 (unattractive) to 7 (attractive). Because attractiveness ratings can vary
by age and sex, raters were instructed to evaluate each participant’s
Table 1
Correlations Among Big Five and Core Self-Evaluations
Measures
Big Five
12-item Core
Self-Evaluations Scale
15-item
current measure
Emotional stability
Extraversion
Openness
Agreeableness
Conscientiousness
.43
.36
.15
.20
.29
.34
.28
.14
.26
.28
SMART, ATTRACTIVE, OR CONFIDENT
attractiveness relative to individuals in the participant’s same age
and gender group.
Six ratings were used to compute an overall assessment of
physical attractiveness. Four of these ratings were from Andreoletti et al. (2001), who had male and female undergraduate students
rate the front and profile photographs of study participants. The
student ratings in the Andreoletti et al. study were supplemented
by ratings from older adults (men and women). Thus, each study
participant had six ratings: (a) undergraduate male ratings of his or
her front photograph, (b) undergraduate male ratings of his or her
profile photograph, (c) undergraduate female ratings of his or
her front photograph, (d) undergraduate female ratings of his or her
profile photograph, (e) older adult male ratings of his or her front
photograph, and (f) older adult female ratings of his or her front
photograph. Thus, we calculated an overall physical attractiveness
score by computing an average score for each participant across
these six sets of ratings. The coefficient alpha reliability estimate
of this scale was .95.
General mental ability. The cognitive testing consisted of nine
measures of cognitive ability completed in the following order: (a)
Wechsler Adult Intelligence Scale (WAIS; Wechsler, 1955) Forward
Digit Span, (b) WAIS Backward Digit Span, (c) WAIS Vocabulary,
(d) counting backwards test, (e) letter comparison test, (f) dual-task
test involving the counting backwards and letter comparison tests, (g)
WAIS Digit Symbol test, (h) Schaie-Thurstone Letter Series test
(Schaie, 1985), and (i) Raven’s Advanced Progressive Matrices test
(Raven, Raven, & Court, 1991). We formed an overall general mental
ability factor by first standardizing the measures, and then computing
a unit-weighted composite of the scores on the nine tests. The reliability of the general mental ability composite was rxx ⫽ .83 and was
calculated with the formula for the reliability of a composite (Nunnally & Bernstein, 1994).
Age, race, and sex. Age, race, and sex were measured at Time
1. Because of the small number of non-White participants in our
sample, we collapsed minority groups into a single category. Race
was coded 1 (White) and 2 (non-White). Sex was coded 1 (male)
and 2 (female).
747
models with alternative models. In estimating the hypothesized
and alternative models, we treated the variables as manifest estimated with measurement error. We corrected for measurement
error by constraining the error term as ␪ε ⫽ ␴2y ⫻ (1 ⫺ ␣y), where
␪ε is the error variance for endogenous variables, ␪␦ is the error
term for exogenous (x) variables, ␴2y is the variance of variable y,
and ␣y is the reliability of variable y.
With covariance structure models, it is essential first to examine
the overall fit of the model. If the model does not fit the data
acceptably, the overall hypothesis that the model is an accurate
representation of the data is rejected. In such cases, the coefficients
estimated in the model can be biased owing to relevant omitted
causes and thus are meaningless (L. R. James, Mulaik, & Brett,
1982). The most widely used measure of model fit is the chi-square
statistic. Hu and Bentler (1995) further suggested a combination of
standardized root-mean-square residual (RMSR) and the comparative fit index (CFI; Bentler, 1990). We also report the root-meansquare error of approximation (RMSEA; MacCallum, Browne, &
Cai, 2006) and the nonnormed fit index (NNFI; MacCallum,
Roznowski, Mar, & Reith, 1994). Finally, we report the Akaike
Information Criterion (AIC; Akaike, 1987) because it is useful for
model comparisons (Tanaka, 1993) and because it adjusts for the
parsimony of a model. As Mulaik, James, and van Alstine (1989)
noted, the AIC “penalizes a model for losses in degrees of freedom
resulting from estimating more parameters, when comparing models according to their lack of fit to the data” (pp. 436 – 437).
Tests of Direct Effects
Figure 2 shows that, supporting Hypotheses 1a, 1b, and 1c,
physical attractiveness significantly influenced income (␥ ⫽ .13,
p ⬍ .05), educational attainment (␥ ⫽ .21, p ⬍ .01), and core
self-evaluations (␥ ⫽ .23, p ⬍ .01). Consistent with Hypotheses
2a, 2b, and 2c, general mental ability also had a positive, significant influence on income (␥ ⫽ .41, p ⬍ .01), educational attainment (␥ ⫽ .51, p ⬍ .01), and core self-evaluations (␥ ⫽ .19, p ⬍
.01).1 In support of Hypotheses 3 and 4, educational attainment
(␤ ⫽ .18, p ⬍ .01) and core self-evaluations (␤ ⫽ .23, p ⬍ .01) had
Results
Control Variables
As shown in Figure 1, we controlled for several relationships
that should be taken into account when considering income attainment. Age, race, and sex tend to be related to income such that
older individuals, Whites, and men earn more on average (Ng et
al., 2005). Also, on the basis of recent research on specific core
traits (Heckman et al., 2006; Waddell, 2006), we expected core
self-evaluations to be positively related to educational attainment.
Covariance Structure Analysis
Table 2 contains the descriptive statistics and correlations
among the study variables. The correlations and standard deviations served as input to LISREL (Version 8.3; Jöreskog & Sörbom,
1993). Covariance structure models have several advantages relevant to this study, including the fact that they correct latent
variables for measurement error; allow estimation of direct, indirect, and total effects; and facilitate comparisons of hypothesized
1
The model is predicated on the assumption that a single general mental
ability (GMA) factor is an appropriate means of conceptualizing and
measuring cognitive ability. To corroborate this assumption, we performed
two analyses. First, we specified a confirmatory factor analysis model in
which a single GMA factor was posited to explain the covariance among
the individual cognitive ability measures. The confirmatory factor analysis
results revealed that the single-factor model fit the data well. The fit
statistics of the model were ␹2(27, N ⫽ 191) ⫽ 49.26, RMSR ⫽ .057,
RMSEA ⫽ .065, NNFI ⫽ .94, CFI ⫽ .95. Each of the nine factor loadings
was significant (t̄ ⫽ 8.17); the average factor loading was ␭¯ x ⫽ .56.
Second, consistent with Ree, Earles, and Teachout (1994), we examined
the degree to which the specific abilities predicted the criterion, controlling
for the overall GMA measure. In conducting such a usefulness analysis
(adding the GMA measure after first controlling for the ability measure and
then reversing this procedure), the GMA measure always predicted income
(nine out of nine times). However, only one of the facets (Letter Series
task) explained additional variance beyond that explained by GMA, and its
standardized coefficient was smaller (␤ ⫽ .21, p ⬍ .05) than GMA (␤ ⫽
.28, p ⬍ .01) even in that case. Across the nine regressions, the average
standardized regression weight of GMA was ␤ ⫽ .40, compared with ␤ ⫽
.05 for the specific ability measures.
JUDGE, HURST, AND SIMON
748
Table 2
Means, Standard Deviations, and Intercorrelations Among Study Variables
Variable
1.
2.
3.
4.
5.
6.
7.
8.
9.
Financial strains
Income
Core self-evaluations
Educational attainment
Physical attractiveness
Racea
Sexb
General mental ability
Age
M
SD
1
2
0.00
26.58
0.00
7.68
3.30
1.07
1.41
0.00
47.89
0.84
5.94
0.35
2.62
0.63
0.25
0.49
0.68
13.75
(.86)
⫺.30ⴱⴱ
⫺.29ⴱⴱ
⫺.17ⴱ
⫺.05
.20ⴱⴱ
.06
⫺.06
⫺.32ⴱⴱ
—
.33ⴱⴱ
.46ⴱⴱ
.24ⴱⴱ
⫺.14ⴱ
⫺.09
.50ⴱⴱ
⫺.16ⴱ
3
(.85)
.22ⴱⴱ
.20ⴱⴱ
⫺.06
⫺.08
.18ⴱ
⫺.06
4
5
6
7
8
9
—
.24ⴱⴱ
⫺.12
⫺.01
.49ⴱⴱ
⫺.07
(.95)
.00
.12
.16ⴱ
⫺.05
—
⫺.02
⫺.22ⴱⴱ
⫺.13
—
⫺.04
⫺.15ⴱ
(.83)
⫺.27ⴱⴱ
—
Note. Where appropriate, coefficient alpha or composite reliability estimates are listed on the diagonal. Listwise N ⫽ 191.
1 ⫽ White, 2 ⫽ non-White. b 1 ⫽ male, 2 ⫽ female.
ⴱ
p ⬍ .05. ⴱⴱ p ⬍ .01.
a
significant effects on income. Consistent with Hypotheses 7a and
7b, income (␤ ⫽ ⫺.31, p ⬍ .01) and core self-evaluations (␤ ⫽
⫺.26, p ⬍ .01) negatively influenced financial strain.
Tests of Partial Mediation
To test the partial mediation hypotheses (Hypotheses 5a, 5b, 6a,
6b, and 7c), we estimated the direct, indirect, and total effects of
physical attractiveness and general mental ability on income and
financial strains. These results are shown in Table 3. Physical
attractiveness had significant direct and indirect effects on income,
and this indirect effect is via education (Hypothesis 5a) and core
self-evaluations (Hypothesis 5b).2 Similarly, general mental ability
had significant direct and indirect effects on income, and this
indirect effect appeared to be explained by education (Hypothesis
6a) and core self-evaluations (Hypothesis 6b), as each of these
variables had direct effects on income. Finally, as shown in Table
3, Hypothesis 7c was supported in that core self-evaluations had
significant direct and indirect effects on financial strains, and the
indirect effect is via the effect of income on financial strains.
Overall, the mediation effects were significant, and the results
clearly supported partial rather than full mediation. Of the total
effects of physical attractiveness and general mental ability on
income, 38.1% and 21.2%, respectively, were indirect. For the
relationship between core self-evaluations and financial strains,
21.2% of the total effect was mediated by income.
Model Fit and Tests of Alternative Models
The fit statistics for the hypothesized model are displayed in
Table 4. As is shown in the table, by conventions used to judge fit
statistics, the hypothesized model appeared to fit the data acceptably in that the chi-square statistic was nonsignificant and the ratio
of chi-square to degrees of freedom was below 3 (Kline, 1998; but
see MacCallum, 1998, for a cautionary note), the standardized
RMSR was less than .08 (Hu & Bentler, 1995), the RMSEA was
below .05 (Browne & Cudeck, 1993), and the CFI and NNFI
statistics were greater than .95 (Hu & Bentler, 1995). The squared
multiple correlations for each structural equation were as follows:
educational attainment, R2 ⫽ .25; core self-evaluations, R2 ⫽ .10;
income, R2 ⫽ .47; financial strain, R2 ⫽ .27.
To further depict how well the model predicted levels of income, we estimated a multinomial logistic regression predicting
the income categories in which race and sex were treated as
categorical predictors and core self-evaluations, education, physical attractiveness, general mental ability, and age were covariates.
The model’s pseudo-squared correlation is .60, and the classification analysis revealed that 32.3% of the observed categories were
correctly predicted with the explanatory variables (because there
are 35 income categories, 2.86% would be correctly predicted by
chance alone). Because the number of income categories was quite
large (ranging from 2 to 36), we also calculated the percentage of
observed income categories that were correctly predicted with
minimal error (⫾1 variance between the predicted and actual
category). In that case, the classification rate was 51.0%. Overall,
the results suggest that the variables predicted the income categories with a reasonable (albeit imperfect) degree of accuracy.
In evaluating a hypothesized model, it is important to compare
its fit to competing models (MacCallum, Wegener, Uchino, &
Fabrigar, 1993). As MacCallum (1998) noted, “It is quite difficult
to evaluate a single model in isolation without a reference point”
(p. 22). We estimated the following alternative models: (a) a distal
effects only model (referred to as Number 1), whereby only general
mental ability and physical attractiveness, and not educational
attainment and core self-evaluations, predicted income (i.e., the
links of educational attainment and core self-evaluations with
income were constrained to zero); (b) an entirely mediated model
(referred to as Number 2), whereby only educational attainment
and core self-evaluations, and not general mental ability and physical attractiveness, directly predicted income (i.e., the direct links
of general mental ability and physical attractiveness with income
were constrained to zero); and (c) an independent effects model
(referred to as Number 3), whereby the four variables (general
mental ability, physical attractiveness, educational attainment, core
2
Because the indirect effect of attractiveness on income encompasses
two mediated pathways— one through educational attainment (Hypothesis 5a) and the other through core self-evaluations (Hypothesis 5b)—we
conducted separate mediation tests using the Sobel (1982) test. Results
indicated that both mediation effects were significant for core selfevaluations (Z ⫽ 2.02, p ⬍ .05) and educational attainment (Z ⫽ 2.58,
p ⬍ .01).
SMART, ATTRACTIVE, OR CONFIDENT
749
.41
41**
General mental
ability
(Test composite)
Educational
attainment
(Time 1)
51**
.51
.18**
.21**
.24**
Demographics
(age/race/sex)
Income
(Time 2)
-.03 / -.02 / -.09
-.31**
Financial strains
(Time 3)
.19
19**
.23**
-.26**
Physical
attractiveness
(ratings)
.23**
Core selfevaluations
(Time 1)
.13
13*
Figure 2.
LISREL results for hypothesized model. ⴱ p ⬍ .05.
self-evaluations) contribute to income independently (i.e., the links
of general mental ability and physical attractiveness with educational attainment, and of physical attractiveness with core selfevaluations, were constrained to zero).
The difference in chi-square between the hypothesized model
and each alternative model was significant (Number 1: ⌬␹22 ⫽
14.73, p ⬍ .01; Number 2: ⌬␹22 ⫽ 21.00, p ⬍ .01; Number 3:
⌬␹23 ⫽ 75.54, p ⬍ .01), indicating that the alternative models
provided a significantly poorer fit to the data than did the hypothesized model. Moreover, the difference between the AIC of the
hypothesized model and the alternative models was also substantial and greater than the differences among the alternative models
(Number 1: ⌬AIC ⫽ 10.73, p ⬍ .01; Number 2: ⌬AIC ⫽ 17.00,
p ⬍ .01; Number 3: ⌬AIC ⫽ 59.54, p ⬍ .01). The AIC is
particularly useful in comparing nested models because it “clearly
represents the penalty logic and encourages investigators to select
the simplest from a range of alternative models” (Tanaka, 1993, p.
22). The other fit statistics for the alternative models also were
poorer than the hypothesized model, although the degree of difference varied by statistic (e.g., the difference for the RMSR was
much smaller than for the CFI or NNFI).3
Discussion
This study finds that, even accounting for intelligence, one’s
income prospects are enhanced by being good-looking. Education
and core self-evaluations are key factors linking attractiveness and
intelligence to income. Although few would doubt that more
intelligent people receive more education, the idea that more
attractive people are better educated is hardly intuitive. Likewise,
it seems reasonable for those who are intelligent to have greater
confidence in their capabilities, whereas basing one’s self-worth
on looks seems ill-founded. Yet, people who are attractive do think
ⴱⴱ
p ⬍ .01.
more highly of their worth and capabilities. This too results in
higher earnings and less financial stress.
We can be somewhat heartened by the fact that the effects of
general mental ability on income were stronger than those of facial
attractiveness. It turns out that the brainy are not necessarily at a
disadvantage to the beautiful, and if one possesses both intelligence and good looks, then all the better. Moreover, the effects of
self-concept are particularly noteworthy. Its effects on income are
stronger than those of attractiveness and nearly as strong as those
of intelligence. The influence of core self-evaluations on both
income and financial strain underlines the critical role it can play
in both objective and subjective life success.
This study makes several contributions. It is one of only a few
that longitudinally links attractiveness to individual differences,
and it is the only one of which we are aware to demonstrate that
individual differences mediate the relationship between attractive3
Although gender was used as a control variable in the model, that does
not address whether the structural equations operate differentially by
gender. Accordingly, we performed a Chow (1960) test on each endogenous variable to determine whether the antecedent variables differentially
predicted the endogenous variables for men and women. In conducting the
Chow tests, we estimated three (one for men, one for women, and one
pooled) regressions for each of the endogenous variables in the model
(income, financial strains, core self-evaluations, and education) using as
independent variables those posited in the model (e.g., with income, six
independent variables were used: race, age, general mental ability, education, core self-evaluations, and physical attractiveness). Although several
Chow test statistics approached significance, none were statistically significant: income, F(6, 184) ⫽ 1.61, p ⫽ .15; financial strain, F(2, 234) ⫽
2.18, p ⫽ .12; core self-evaluations, F(2, 208) ⫽ 1.52, p ⫽ .22; and
education, F(2, 228) ⫽ 0.64, p ⫽ .53. These results suggest that the
structural equations do not predict the endogenous variables differentially
for men and women.
JUDGE, HURST, AND SIMON
750
Table 3
Direct, Indirect, and Total Effects of Variables on Income and Financial Strains
Income
Variable
Direct
Core self-evaluations
Educational attainment
Physical attractiveness
General mental ability
.23ⴱⴱ
.18ⴱⴱ
.13ⴱ
.41ⴱⴱ
ⴱ
p ⬍ .05.
ⴱⴱ
Financial strains
Indirect
Total
Direct
Indirect
Total
⫺.26ⴱⴱ
.08ⴱⴱ
.11ⴱⴱ
.23ⴱⴱ
.18ⴱⴱ
.21ⴱⴱ
.52ⴱⴱ
⫺.07ⴱ
⫺.05
⫺.13ⴱⴱ
⫺.22ⴱⴱ
⫺.33ⴱⴱ
⫺.05
⫺.13ⴱⴱ
⫺.22ⴱⴱ
p ⬍ .01.
ness and income. Second, it casts light on the relative influence of
attractiveness and intelligence on income attainment. Finally, it
enhances our understanding of both the foundations of global
self-concept and its consequences. Overall, this study enhances the
complexity of our understanding with regard to what factors differentiate one’s level of success from that of another.
Future Research
Considering that only 43% of the effects of attractiveness were
explained by core self-evaluations and educational attainment,
other possible mechanisms should be explored. In particular, although there has been broad examination of the types of bias faced
by unattractive people in employment contexts (Hosoda et al.,
2003; Langlois et al., 2000), there is still room for exploration of
characteristics that could serve as sources of the income difference.
For instance, attractive people tend to be higher in extraversion
(Langlois et al., 2000), which is positively associated with transformational leadership behaviors (Bono & Judge, 2004). It would
be interesting to examine whether actual trait and behavioral
differences interact with biased perceptions of the leadership capabilities of attractive people to result in greater opportunities for
advancement and higher earnings.
Implicit leadership theory (Lord, Foti, & Phillips, 1982) argues
that people develop schemas of characteristics that indicate an
effective leader. Individuals are categorized and evaluated as leaders to the extent that they are prototypical of others’ schemas. In
addition to being more extraverted, attractive people tend to be
seen as higher in intelligence (Jackson, Hunter, & Hodge, 1995;
Langlois et al., 2000), although the actual correlation between
attractiveness and intelligence is negligible (␳ ⫽ .03; Langlois et
al., 2000). Perceptual measures of intelligence are more positively
related to leader emergence than paper-and-pencil measures
(Judge, Colbert, & Ilies, 2004), indicating that those who are seen
as intelligent are more likely to become leaders. Attractive people
are also viewed as less self-serving when engaging in efforts to
wield influence over others (Reinhard, Messner, & Sporer, 2006),
which suggests that people would be more receptive to their
attempts to attain leadership positions.
Attractive people may also develop greater social capital, which
is tied to career success (Seibert, Kraimer, & Liden, 2001). They
tend to be more popular (Langlois et al., 2000), and Dollinger
(2002) found that attractive people depicted themselves as more
socially connected than less attractive people. Also, Mulford,
Orbell, Shatto, and Stockard (1998) found that others are more
likely to cooperate with attractive people, partly because the latter
are expected to be more cooperative. Future research might link
attractiveness to the characteristics of individuals’ personal and
professional networks. It would also be interesting to examine
whether attractive individuals have higher quality dyadic relationships with coworkers and, particularly, supervisors. It is possible
that affective reactions to attractive individuals are more positive,
even to the extent of mitigating the effects of an individual’s
weaknesses.
Attractiveness might also have some less than beneficial effects
on personality and behavior. For instance, given the level of
attention they receive, attractive people could be more narcissistic,
Table 4
Fit Statistics for Hypothesized and Alternative Models
Model
Hypothesized model
Alternative 1 (distal effects only)
Dropping links of educational attainment and
CSE with income
Alternative 2 (entirely mediated)
Dropping direct links of GMA and physical
attractiveness with income
Alternative 3 (independent effects)
Dropping links of physical attractiveness with
educational attainment and CSE and of
GMA with educational attainment
␹2
df
␹2/df
RMSR
RMSEA
CFI
NNFI
AIC
38.58
30
1.29
.07
.04
.96
.96
68.58
53.31
32
1.67
.08
.06
.91
.90
79.31
59.58
32
1.86
.08
.07
.89
.87
85.58
114.12
33
3.46
.16
.11
.66
.63
128.12
Note. RMSR ⫽ standardized root-mean-square residual; RMSEA ⫽ root-mean-square error of approximation; CFI ⫽ comparative fit index; NNFI ⫽
nonnormed fit index; AIC ⫽ Akaike Information Criterion; CSE ⫽ core self-evaluations; GMA ⫽ general mental ability.
SMART, ATTRACTIVE, OR CONFIDENT
which is negatively related to leadership effectiveness, task performance, and organizational citizenship behaviors (Judge,
LePine, & Rich, 2006). Also, because of the benefits beauty has
bestowed on them, attractive people might be more psychically
invested in their looks. There is evidence that some people base
more of their self-worth than others on their appearance, and as
noted earlier, those who do tend to have more financial problems
(Crocker & Luhtanen, 2003). If attractiveness also promotes characteristics that are negatively associated with job performance and
income, then there might be little net effect of appearance on
income via traits and behaviors. The income advantage of attractive people could be entirely due to bias. Only further research can
reveal to what extent individual differences are responsible for the
income advantages that accrue to good-looking people relative to
stereotyping in the labor market.
The astute reader will notice that our treatment of core selfevaluations as a dependent variable is unusual. Nearly all core
self-evaluations research considers them an independent variable
and generally labels core self-evaluations a trait (Judge, 2009).
Indeed, at first blush, our own reference to core self-evaluations as
a trait would appear to belie our treatment of them as an endogenous variable (influenced by attractiveness and general mental
ability). We believe this consideration of core self-evaluations as
both a trait and an endogenous variable is not the contradiction it
might seem, for both a general reason and a more specific one.
First, all traits—the Big Five included— can be considered to
have state (within person, situational) and trait (between individuals, temperamental) properties (Fleeson, 2004). Though the trait
properties of the Big Five traits are well accepted in both applied
and personality psychology, less recognized in applied psychology
is that measures of the traits show evidence of change over time
(Roberts et al., 2006). Roberts and Mroczek (2008) noted, “Research now shows that personality traits continue to change in
adulthood and often into old age, and that these changes may be
quite substantial and consequential” (p. 31). More is being learned
about why traits change, with work experiences figuring prominently in the equation (Roberts, Caspi, & Moffitt, 2003). As
Roberts, Caspi, and Moffitt (2003) commented, “The perspective
that traits do not change in response to life experiences is developmentally incomplete” (p. 591).
Second, in specific reference to core self-evaluations, although
their statelike qualities have not been sufficiently explored in past
research, there is a voluminous body of research with respect to the
specific trait with which the broad core self-evaluations factor
correlates most highly—self-esteem (Judge et al., 2003). In personality and social psychology, one finds genetic studies on selfesteem (Raevuori et al., 2007), and one also finds studies in which
changes in self-esteem are produced from experimental manipulations (Leary, Haupt, Strausser, & Chokel, 1998). As recently
noted by Denissen, Penke, Schmitt, and van Aken (2008), these
trait and state aspects of self-esteem coexist between and within
individuals.
As the foregoing discussion indicates, all traits, including core
self-evaluations, can productively be considered for their statelike,
conditional characteristics. Nevertheless, future research should
investigate the dynamic nature of core self-evaluations, including
their viability as both a dependent and an independent variable and
their possible within-individual variation. Moreover, more research is needed on the temperamental nature (i.e., the possible
751
genetic basis) of core self-evaluations. As with research on the Big
Five traits, we do not see these lines of research as incompatible.
Practical Implications
Some might take the results presented in this article to mean that
stereotypes favoring attractive people in employment contexts are
well founded. It is true that there seem to be differences among
people at varying levels of attractiveness in characteristics that
influence career success. All the same, these relationships are not
strong enough to justify using attractiveness as a proxy for less
tangible attributes, particularly given that educational attainment
is easy enough to verify and reliable measures are available
for traits like core self-evaluations (Judge et al., 2003). Furthermore, there is ample evidence that decision makers will privilege
attractiveness, regardless of the amount of other job-relevant information they have about the people subject to their decisions
(Hosoda et al., 2003). Thus, in the interests of fairness and effectiveness, it is still worthwhile for employers to make an effort to
reduce the effects of bias toward attractive people in the workplace. Nevertheless, the elimination of stereotyping among employers would not entirely overcome the beauty premium. Assuming that the differences in education and core self-evaluations
found here are the result of treatment early in life, bias would have
to be rooted out long before people reach the labor market. After
all, preferential treatment based on attractiveness begins with the
behaviors of mothers (Langlois et al., 1995) and other caregivers
(Casey & Ritter, 1996; Ritter, Casey, & Langlois, 1991) toward
infants.
If the growth of the beauty and cosmetic surgery industries is
any indication, the lengths to which people will go to enhance their
appearance are heightening. In the United States, cosmetic procedures tripled from 1997 to 2003, and globally the beauty industry
is growing at a faster rate than the growth domestic product of
most developed countries (“Pots of Promise,” 2003). In China, it is
becoming more common for people to undergo loan-financed
cosmetic surgery explicitly to enhance their income prospects
(“Saving Face,” 2004). Yet, our results suggest that people could
achieve, at less cost, some of the financial benefits found among
people who are highly attractive by striving to develop certain
personal characteristics that influence labor market success. As
mentioned, the higher levels of education attained by attractive
people are not due to meaningful underlying intelligence differences (Jackson et al., 1995; Langlois et al., 2000), nor is their
greater self-confidence due to any difference in intrinsic value. Of
course, given the rising costs of college, beauty treatments and
cosmetic surgery could seem less expensive (albeit more risky);
however, in our findings, education exerted a stronger direct effect
on earnings than attractiveness, indicating that working to increase
the former has greater payoffs.
The value of trying to increase one’s self-worth is more difficult
to assess. Some scholars argue that the pursuit of self-worth is
dangerous because failure in areas on which self-esteem is contingent is so emotionally damaging (Baumeister, Campbell,
Krueger, & Vohs, 2003; Crocker & Park, 2004). Others counter
that self-worth can be pursued adaptively (DuBois & Flay, 2004),
although further research is needed to understand specific change
mechanisms (Swann, Chang-Schneider, & Larsen-McClarty,
2007).
752
JUDGE, HURST, AND SIMON
Limitations
One weakness of this study is the fact that attractiveness was
measured in adulthood, although we argued that the mediating
processes are due to expectancy effects that start in childhood and
adolescence, as many other researchers have argued. This is a
limitation of the archival data set we used. However, facial attractiveness in childhood is positively correlated with attractiveness
through adulthood. Although absolute levels of attractiveness do
tend to decline, relative rankings remain fairly stable (Zebrowitz,
Olson, & Hoffman, 1993). Therefore, it is probable that the attractiveness ratings of participants in our sample resemble those that
could have been obtained during childhood.
Second, we could not demonstrate that the effects of attractiveness on education and core self-evaluations were actually due to
discriminatory treatment on the basis of appearance. It seems
likely, given strong evidence that attractive people are treated
differently (Langlois et al., 2000). However, further longitudinal
research that links attractiveness to treatment and treatment to
individual differences would provide stronger support for arguments that appearance exerts effects through expectancy confirmation processes.
Another limitation is that our measure of income, like most
income measures used in the literature, was self-reported. Although measures of self-reported income are often more prone to
measurement error than are archival measures, past research on the
issue has found these differences to be small and that similar
results are obtained with self- and archival reports of salary
(Bollinger, 1998; Graham, Hotchkiss, & Gerhart, 2000). Moreover, evidence indicates that self-reported and archival measures
of income are substantially correlated (Judge, Cable, Boudreau, &
Bretz, 1995).
Finally, one of the strengths of our study is that our sample
allows for generalization beyond a specific company or profession.
However, we recognize that because the sample was racially
homogeneous, how well these results generalize to more diverse
populations is unknown. For instance, the small percentage of
minorities in our study is well below the national average of 24.9%
(U.S. Census Bureau, 2000). Additionally, compared with national
averages, larger percentages of our sample were married and were
more highly educated. Given that prior research assessing the
potential moderating effects of demographic variables on the relationships between physical attractiveness and personality and job
outcomes is mixed (see, e.g., Frieze, Olson, & Russell, 1991;
Hamermesh & Biddle, 1994; Hosoda et al., 2003; Lerner & Karabenick, 1974; Parks & Kennedy, 2007), the relationships between
variables in our study should be investigated among more demographically heterogeneous samples before results are confidently
extended to such populations.
Conclusion
We live in a world as captivated by beauty as it is uncomfortable
with the advantages beauty confers. Even as we remind one
another that it is only skin deep, we pursue beauty with a formidable devotion. The results of this study further attest to the
importance of physical attractiveness, by showing that it influences
self-concept (core self-evaluations), income, and financial wellbeing (financial strain). As economic inequality grows (Fessenden,
2007), scholars are turning their attention to the skills that produce
inequality in our society. Cunha and Heckman (2007) noted, “It is
well documented that people have diverse abilities, [and] these
abilities account for a substantial portion of the variation across
people in socioeconomic success” (p. 31). Our study suggests that
although skill (in the form of cognitive ability) clearly matters to
financial success, attractiveness matters too. The advantages conferred by beauty may have originated from early human evolution
(Todd, Penke, Fasolo, & Lenton, 2007). As society continues to
advance and evolve, it is interesting, and somewhat troubling, to
observe that such primitive instincts still play an important role in
life outcomes.
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Received September 8, 2007
Revision received September 30, 2008
Accepted October 27, 2008 䡲